R Notebook
Load frequently used packages
library(skimr)
library(openxlsx)
library(ggrepel)
library(dplyr)
library(tidyr)
library(data.table)
library(broom)
library(broomExtra)
library(tibble)
library(sjstats)
library(car)
library(lme4)
library(lmerTest)
library(ggplot2)
library(tibble)
library(modelr)
library(tidyverse)
#library(miceadds)
library(ggforce)
require(openxlsx)
library(tidyverse)
library(caret)
library(glmnet)
library(ggplot2)
library(gridExtra)
library(MASS) # rlm
library(lmPerm)
library(circlize)
library(RColorBrewer)
library(ComplexHeatmap)
select <- dplyr::select
filter <- dplyr::filterDefine the directories used in this script
dir.IlluminaProductFiles <- "/Users/shawjes/Dropbox/EspinosaGroup/DATA_MAIN/MEGA/chr21_genotyping/Illumina_ProductFiles"
dir.chr21_genotyping <- "/Users/shawjes/Dropbox/EspinosaGroup/DATA_MAIN/MEGA/chr21_genotyping"
dir.chr21_idatQuants <- "/Users/shawjes/Dropbox/EspinosaGroup/DATA_MAIN/MEGA/chr21_genotyping/idatQuants"
dir.GRSdata <- "/Users/shawjes/Dropbox/EspinosaGroup/ANALYSIS/Celiac_MultiOmics/GRS/DSMIG_Shared/Manuscript_Figure1/Data"
dir.GRSplots <- "/Users/shawjes/Dropbox/EspinosaGroup/ANALYSIS/Celiac_MultiOmics/GRS/DSMIG_Shared/Manuscript_Figure1/Plots"
dir.GRSoriginal.Anno <- "/Users/shawjes/Dropbox/EspinosaGroup/ANALYSIS/Celiac_MultiOmics/GRS/DSMIG_Shared/Manuscript_Figure1/Annotation/GRSoriginal"
dir.GRSrevised.Anno <- "/Users/shawjes/Dropbox/EspinosaGroup/ANALYSIS/Celiac_MultiOmics/GRS/DSMIG_Shared/Manuscript_Figure1/Annotation/GRSrevised"
dir.PCA <- '/Users/shawjes/Dropbox/EspinosaGroup/ANALYSIS/Celiac_MultiOmics/GRS/DSMIG_Shared/Manuscript_Figure1/Data/PCA'
dir.Results <- '/Users/shawjes/Dropbox/EspinosaGroup/ANALYSIS/Celiac_MultiOmics/GRS/DSMIG_Shared/Manuscript_Figure1/Results'Print session info for reproducibility
save.sessionInfo <- sessionInfo()
save.sessionInfoR version 4.1.1 (2021-08-10)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.7
Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] grid stats graphics grDevices utils datasets methods base
other attached packages:
[1] glmnet_4.1-3 lmerTest_3.1-3 lme4_1.1-29 Matrix_1.4-1 ComplexHeatmap_2.8.0 RColorBrewer_1.1-3 circlize_0.4.14
[8] lmPerm_2.1.0 MASS_7.3-56 gridExtra_2.3 caret_6.0-91 lattice_0.20-45 ggforce_0.3.3 modelr_0.1.8
[15] car_3.0-12 carData_3.0-5 sjstats_0.18.1 broomExtra_4.3.2 broom_0.7.12 ggrepel_0.9.1 openxlsx_4.2.5
[22] skimr_2.1.3 forcats_0.5.1 stringr_1.4.0 dplyr_1.0.8 purrr_0.3.4 readr_2.1.2 tidyr_1.2.0
[29] tibble_3.1.6 ggplot2_3.3.5 tidyverse_1.3.1 data.table_1.14.2
loaded via a namespace (and not attached):
[1] readxl_1.4.0 backports_1.4.1 plyr_1.8.7 repr_1.1.4 splines_4.1.1 listenv_0.8.0 TH.data_1.1-0
[8] digest_0.6.29 foreach_1.5.2 htmltools_0.5.2 fansi_1.0.3 magrittr_2.0.3 cluster_2.1.3 doParallel_1.0.17
[15] tzdb_0.3.0 recipes_0.2.0 globals_0.14.0 gower_1.0.0 matrixStats_0.61.0 sandwich_3.0-1 hardhat_0.2.0
[22] colorspace_2.0-3 rvest_1.0.2 haven_2.4.3 xfun_0.30 crayon_1.5.1 jsonlite_1.8.0 survival_3.3-1
[29] zoo_1.8-9 iterators_1.0.14 glue_1.6.2 polyclip_1.10-0 gtable_0.3.0 ipred_0.9-12 emmeans_1.7.3
[36] GetoptLong_1.0.5 sjmisc_2.8.9 shape_1.4.6 future.apply_1.8.1 BiocGenerics_0.38.0 abind_1.4-5 scales_1.2.0
[43] mvtnorm_1.1-3 DBI_1.1.2 Rcpp_1.0.8.3 xtable_1.8-4 performance_0.9.0 clue_0.3-60 stats4_4.1.1
[50] lava_1.6.10 prodlim_2019.11.13 datawizard_0.4.0 httr_1.4.2 ellipsis_0.3.2 pkgconfig_2.0.3 farver_2.1.0
[57] nnet_7.3-17 sass_0.4.1 dbplyr_2.1.1 utf8_1.2.2 tidyselect_1.1.2 rlang_1.0.2 reshape2_1.4.4
[64] effectsize_0.6.0.1 munsell_0.5.0 cellranger_1.1.0 tools_4.1.1 cli_3.2.0 generics_0.1.2 sjlabelled_1.1.8
[71] evaluate_0.15 fastmap_1.1.0 yaml_2.3.5 ModelMetrics_1.2.2.2 knitr_1.38 fs_1.5.2 zip_2.2.0
[78] future_1.24.0 nlme_3.1-157 xml2_1.3.3 compiler_4.1.1 rstudioapi_0.13 png_0.1-7 reprex_2.0.1
[85] tweenr_1.0.2 bslib_0.3.1 stringi_1.7.6 parameters_0.17.0 nloptr_2.0.0 vctrs_0.4.0 pillar_1.7.0
[92] lifecycle_1.0.1 jquerylib_0.1.4 GlobalOptions_0.1.2 estimability_1.3 insight_0.17.0 R6_2.5.1 IRanges_2.26.0
[99] parallelly_1.30.0 codetools_0.2-18 boot_1.3-28 assertthat_0.2.1 rjson_0.2.21 withr_2.5.0 S4Vectors_0.30.2
[106] multcomp_1.4-18 bayestestR_0.11.5 parallel_4.1.1 hms_1.1.1 rpart_4.1.16 timeDate_3043.102 coda_0.19-4
[113] class_7.3-20 minqa_1.2.4 rmarkdown_2.13 Cairo_1.5-15 pROC_1.18.0 numDeriv_2016.8-1.1 lubridate_1.8.0
[120] base64enc_0.1-3 Setting and modifying theme for plots
theme_set(theme_gray(base_size = 12, base_family = "Arial") +
theme(panel.border = element_rect(colour="black", fill = "transparent"),
plot.title = element_text(face="bold", hjust = 0), # lineheight=.8, size=20,
axis.text = element_text(color="black", size = 14),
axis.text.x = element_text(angle = 0, hjust = NULL),
strip.background = element_rect(colour="black", fill = "light grey", size = 1), # adjusts facet label borders (if any)
panel.background = element_blank(),
panel.grid = element_blank()
))
RedBlue <- c("#CD3333", "#1874CD")
GrayBlue <- c("grey", "#2b8cbe")Initialize an object to store the column names for the identifiers we want to keep in each analysis dataframe
ID_colnames <- c("FamilyID", "RecordID", "MEGA.FID", "MEGA.IID", "MEGA.LabID")Read in prepared metadata
setwd(dir.GRSdata)Warning: The working directory was changed to /Users/shawjes/Dropbox/EspinosaGroup/ANALYSIS/Celiac_MultiOmics/GRS/DSMIG_Shared/Manuscript_Figure1/Data inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.analysis_meta <- fread("MEGA_041822_META_CeliacGRS_v0.1_JRS.csv") %>%
select(ID_colnames, Celiac, LabID, Celiac, EXCLUDE_from_analysis, EXCLUDE_reason) %>%
unique()Note: Using an external vector in selections is ambiguous.
ℹ Use `all_of(ID_colnames)` instead of `ID_colnames` to silence this message.
ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
This message is displayed once per session.analysis_metaRead in prepared analysis datasets and keep only the IDs included in analysis
setwd(dir.GRSdata)Warning: The working directory was changed to /Users/shawjes/Dropbox/EspinosaGroup/ANALYSIS/Celiac_MultiOmics/GRS/DSMIG_Shared/Manuscript_Figure1/Data inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.analysisData.GRSorig <- fread("MEGA_041822_AnalysisData_CDGRS_Sharp2019_v0.1_JRS.csv") %>%
left_join(analysis_meta, by = ID_colnames) %>%
filter(EXCLUDE_from_analysis == 0)
analysisData.GRSrevised <- fread("MEGA_041822_AnalysisData_CDGRS_Sharp2022_v0.1_JRS.csv") %>%
left_join(analysis_meta, by = ID_colnames) %>%
filter(EXCLUDE_from_analysis == 0)
analysisData.GRSoriganalysisData.GRSrevisedCheck that AFTER applying the EXCLUDE_from_analysis filter, unique IIDs x unique variants = nrows
analysisData.GRSorig %>% nrow()[1] 9996(analysisData.GRSorig %>% select(`Variant (Sharp 2019)`) %>% unique() %>% nrow())*(analysisData.GRSorig %>% select(RecordID) %>% unique() %>% nrow())[1] 9996analysisData.GRSrevised %>% nrow()[1] 9996(analysisData.GRSrevised %>% select(`Variant (Sharp 2022)`) %>% unique() %>% nrow())*(analysisData.GRSrevised %>% select(RecordID) %>% unique() %>% nrow())[1] 9996Read in prepared ancestry PCs
setwd(dir.PCA)Warning: The working directory was changed to /Users/shawjes/Dropbox/EspinosaGroup/ANALYSIS/Celiac_MultiOmics/GRS/DSMIG_Shared/Manuscript_Figure1/Data/PCA inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.ancestry_PCs <- fread("MEGA_041822_Espinosa_MEGA2_HTP_GS_08132019_updated_callrate_passing_QC_KEEPforHLAvsCeliac_EXCLUDEvariants_mind05_geno0.02_maf0.05_PRUNEDindeppairwise0.2_v0.1_JRS.eigenvec") %>%
select(FID, IID, PC1, PC2, PC3, PC4, PC5) %>%
rename(MEGA.FID = FID, MEGA.IID = IID)
ancestry_PCsAdd PCs for ancestry and prepare the dataframes for analysis of GRS vs. Celiac status
analysisData.GRS2019 <- analysisData.GRSorig %>%
select(GRS_version, GRS_reference,
ID_colnames, Celiac,
GRS) %>%
unique() %>%
left_join(ancestry_PCs, by = c("MEGA.FID", "MEGA.IID"))
analysisData.GRS2022 <- analysisData.GRSrevised %>%
select(GRS_version, GRS_reference,
ID_colnames, Celiac,
GRS) %>%
unique() %>%
left_join(ancestry_PCs, by = c("MEGA.FID", "MEGA.IID"))
analysisData.GRS2019analysisData.GRS2022Prepare dataframes for analysis of individual GRS variants vs. Celiac status
analysisData.GRSvariants2019 <- analysisData.GRSorig %>%
select(GRS_version, GRS_reference,
ID_colnames, Celiac,
`Variant (Sharp 2019)`, Score_Allele, Dosage_for_GRS,
`Putative Gene (Sharp 2019)`) %>%
unique() %>%
left_join(ancestry_PCs, by = c("MEGA.FID", "MEGA.IID"))
analysisData.GRSvariants2022 <- analysisData.GRSrevised %>%
select(GRS_version, GRS_reference,
ID_colnames, Celiac,
`Variant (Sharp 2022)`, Score_Allele, Dosage_for_GRS,
`Putative Gene (Sharp 2019)`) %>%
unique() %>%
left_join(ancestry_PCs, by = c("MEGA.FID", "MEGA.IID"))
analysisData.GRSvariants2019analysisData.GRSvariants2022
# Check that nrows still makes sense
analysisData.GRSvariants2019 %>% nrow()[1] 9996(analysisData.GRSvariants2019 %>% select(`Variant (Sharp 2019)`) %>% unique() %>% nrow())*(analysisData.GRSvariants2019 %>% select(RecordID) %>% unique() %>% nrow())[1] 9996analysisData.GRSvariants2022 %>% nrow()[1] 9996(analysisData.GRSvariants2022 %>% select(`Variant (Sharp 2022)`) %>% unique() %>% nrow())*(analysisData.GRSvariants2022 %>% select(RecordID) %>% unique() %>% nrow())[1] 9996Check that we have the expected number of Celiac cases and controls in each analysis dataset
analysisData.GRS2019 %>%
select(RecordID, Celiac) %>%
unique() %>%
group_by(Celiac) %>%
summarise(N = n())
analysisData.GRS2022 %>%
select(RecordID, Celiac) %>%
unique() %>%
group_by(Celiac) %>%
summarise(N = n())
analysisData.GRSvariants2019 %>%
select(RecordID, Celiac) %>%
unique() %>%
group_by(Celiac) %>%
summarise(N = n())
analysisData.GRSvariants2022 %>%
select(RecordID, Celiac) %>%
unique() %>%
group_by(Celiac) %>%
summarise(N = n())Check that # variants * # individuals = # rows
length(unique(analysisData.GRSvariants2019$MEGA.IID))[1] 204length(unique(analysisData.GRSvariants2019$`Variant (Sharp 2019)`))[1] 49identical(nrow(analysisData.GRSvariants2019),
length(unique(analysisData.GRSvariants2019$MEGA.IID))*length(unique(analysisData.GRSvariants2019$`Variant (Sharp 2019)`)))[1] TRUElength(unique(analysisData.GRSvariants2022$MEGA.IID))[1] 204length(unique(analysisData.GRSvariants2022$`Variant (Sharp 2022)`))[1] 49identical(nrow(analysisData.GRSvariants2022),
length(unique(analysisData.GRSvariants2022$MEGA.IID))*length(unique(analysisData.GRSvariants2022$`Variant (Sharp 2022)`)))[1] TRUEVerify that the two versions of GRS are not identical
Although the score weights do not change from GRS 2019 to GRS 2022, the dosage values for SNPs that were replaced are not identical. Therefore, the GRS calculated from the 2019 SNP list should be slightly different from the GRS calculated from the 2022 SNP list.
analysisData.GRS2019 %>%
select(MEGA.IID, GRS) %>%
unique() %>%
rename(GRS_2019 = GRS) %>%
full_join(analysisData.GRS2022 %>%
select(MEGA.IID, GRS) %>%
unique() %>%
rename(GRS_2022 = GRS),
by = "MEGA.IID") %>%
mutate(abs_Delta_GRS = abs(GRS_2022 - GRS_2019)) %>%
arrange(desc(abs_Delta_GRS))
# Good.View the four analysis dataframes to be used as input to logistic regression models
analysisData.GRS2019
analysisData.GRS2022
analysisData.GRSvariants2019
analysisData.GRSvariants2022Run logistic regression to test for association of GRS with Celiac status among T21s
set.seed(1234)
fit.GRS2019 <- glm(Celiac ~ GRS,
family = binomial(link = "logit"),
data = analysisData.GRS2019)
set.seed(1234)
fit.GRS2022 <- glm(Celiac ~ GRS,
family = binomial(link = "logit"),
data = analysisData.GRS2022)
results.GRS2019 <- fit.GRS2019 %>% tidy(conf.int = TRUE) %>%
select(-c(std.error, statistic)) %>%
rename(Term = term,
logOR = estimate,
conf.low_logOR = conf.low,
conf.high_logOR = conf.high,
P = p.value) %>%
mutate(OR = exp(logOR),
conf.low_OR = exp(conf.low_logOR),
conf.high_OR = exp(conf.high_logOR)) %>%
#select(-c(logOR, conf.low_logOR, conf.high_logOR)) %>%
mutate(`95% CI` = paste("(", round(conf.low_OR, digits = 2), ", ", round(conf.high_OR, digits = 2), ")", sep = "")) %>%
mutate(OR = round(OR, digits = 2),
P = round(P, digits = 4)) %>%
filter(Term == "GRS") %>%
mutate(Term = gsub("GRS", "GRS*", Term)) %>%
select(Term, logOR, OR, `95% CI`, P)
results.GRS2022 <- fit.GRS2022 %>% tidy(conf.int = TRUE) %>%
select(-c(std.error, statistic)) %>%
rename(Term = term,
logOR = estimate, # The 'estimate' column from a logistic regression represents the logOR
conf.low_logOR = conf.low,
conf.high_logOR = conf.high,
P = p.value) %>%
mutate(OR = exp(logOR), # OR = e^OR = exp(OR)
conf.low_OR = exp(conf.low_logOR),
conf.high_OR = exp(conf.high_logOR)) %>%
#select(-c(logOR conf.low_logOR, conf.high_logOR)) %>%
mutate(`95% CI` = paste("(", round(conf.low_OR, digits = 2), ", ", round(conf.high_OR, digits = 2), ")", sep = "")) %>%
mutate(OR = round(OR, digits = 2),
P = round(P, digits = 4)) %>%
filter(Term == "GRS") %>%
mutate(Term = gsub("GRS", "GRS*", Term)) %>%
select(Term, logOR, OR, `95% CI`, P)
results.GRS2019results.GRS2022
setwd(dir.Results)Warning: The working directory was changed to /Users/shawjes/Dropbox/EspinosaGroup/ANALYSIS/Celiac_MultiOmics/GRS/DSMIG_Shared/Manuscript_Figure1/Results inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.fwrite(results.GRS2019, "MEGA_041822_RESULTS_CDGRS2019_GRS_vs_Celiac_v0.1_JRS.csv")
fwrite(results.GRS2022, "MEGA_041822_RESULTS_CDGRS2022_GRS_vs_Celiac_v0.1_JRS.csv")Set theme and colors for plots
theme_set(theme_gray(base_size = 12, base_family = "Arial") +
theme(panel.border = element_rect(colour="black", fill = "transparent"),
plot.title = element_text(face="bold", hjust = 0), # lineheight=.8, size=20,
axis.text = element_text(color="black", size = 11),
axis.text.x = element_text(angle = 0, hjust = NULL),
strip.background = element_rect(colour="black", fill = "light grey", size = 1), # adjusts facet label borders (if any)
panel.background = element_blank(),
panel.grid = element_blank()
))
GrayBlue <- c("grey", "#2b8cbe")
RedBlue <- c("#CD3333", "#1874CD")Plot the distribution of modified GRS among T21s with vs. without Celiac
theme_set(theme_gray(base_size = 12, base_family = "Arial") +
theme(panel.border = element_rect(colour="black", fill = "transparent"),
plot.title = element_text(face="bold", hjust = 0, size = 12), # lineheight=.8, size=20,
axis.text = element_text(color="black", size = 11),
axis.text.x = element_text(angle = 0, hjust = NULL),
strip.background = element_rect(colour="black", fill = "light grey", size = 1), # adjusts facet label borders (if any)
panel.background = element_blank(),
panel.grid = element_blank()
))
analysisData.GRS2019$GRS %>% summary() # xlim(-1, 6.5) Min. 1st Qu. Median Mean 3rd Qu. Max.
-0.653 1.016 2.055 2.437 3.894 6.490 # Min. 1st Qu. Median Mean 3rd Qu. Max.
# -0.653 1.016 2.055 2.437 3.894 6.490
analysisData.GRS2022$GRS %>% summary() # xlim(-1, 6.5) Min. 1st Qu. Median Mean 3rd Qu. Max.
-0.653 1.016 2.050 2.449 3.966 6.490 # Min. 1st Qu. Median Mean 3rd Qu. Max.
# -0.653 1.016 2.050 2.449 3.966 6.490
analysisData.GRS2019 %>%
mutate(Group = ifelse(Celiac == 0, "DS without CD", ifelse(Celiac == 1, "DS with CD", NA)),
Group = factor(Group, levels = c("DS without CD", "DS with CD"))) %>%
ggplot(aes(x = GRS, y = ..scaled.., color = Group, fill = Group)) +
geom_density(alpha = 0.5) +
scale_colour_manual(values = GrayBlue) +
scale_fill_manual(values = GrayBlue) +
theme(legend.position = "bottom",
legend.title = element_blank(),
plot.caption = element_text(hjust = 0),
aspect.ratio = 1) +
xlab("GRS*") +
labs(title = "Celiac GRS* in DS +/- CD",
subtitle = paste("OR = ", results.GRS2019$OR[1], " ", results.GRS2019$`95% CI`[1], "\nP<", results.GRS2019$P[1], sep = ""),
caption = "*Sharp et al., 2019")
filename <- "MEGA_041822_DensityPlot_GRS2019_by_CeliacStatus_v0.1_JRS"
setwd(dir.GRSplots)Warning: The working directory was changed to /Users/shawjes/Dropbox/EspinosaGroup/ANALYSIS/Celiac_MultiOmics/GRS/DSMIG_Shared/Manuscript_Figure1/Plots inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.ggsave(filename = paste(filename, ".png", sep = ""), width = 3.6, height = 3.5, units = "in")setwd(dir.GRSplots)
ggsave(filename = paste(filename, ".pdf", sep = ""), device = cairo_pdf, width = 3.6, height = 3.5, units = "in")
analysisData.GRS2022 %>%
mutate(Group = ifelse(Celiac == 0, "DS without CD", ifelse(Celiac == 1, "DS with CD", NA)),
Group = factor(Group, levels = c("DS without CD", "DS with CD"))) %>%
ggplot(aes(x = GRS, y = ..scaled.., color = Group, fill = Group)) +
geom_density(alpha = 0.5) +
scale_colour_manual(values = GrayBlue) +
scale_fill_manual(values = GrayBlue) +
theme(legend.position = "bottom",
legend.title = element_blank(),
plot.caption = element_text(hjust = 0),
aspect.ratio = 1) +
xlab("GRS*") +
labs(title = "Celiac GRS* in DS +/- CD",
subtitle = paste("OR = ", results.GRS2022$OR[1], " ", results.GRS2022$`95% CI`[1], "\nP<", results.GRS2022$P[1], sep = ""),
caption = "*Sharp et al., 2022")
filename <- "MEGA_041822_DensityPlot_GRS2022_by_CeliacStatus_v0.1_JRS"
setwd(dir.GRSplots)
ggsave(filename = paste(filename, ".png", sep = ""), width = 3.6, height = 3.5, units = "in")setwd(dir.GRSplots)
ggsave(filename = paste(filename, ".pdf", sep = ""), device = cairo_pdf, width = 3.6, height = 3.5, units = "in")
Plot ROC curves for GRS in DS
For original GRS (2019)
# AUC from pROC::roc()
library(pROC)Type 'citation("pROC")' for a citation.
Attaching package: ‘pROC’
The following object is masked from ‘package:sjstats’:
ci
The following objects are masked from ‘package:stats’:
cov, smooth, varresRoc2019 <- roc(analysisData.GRS2019$Celiac ~ fit.GRS2019$fitted)Setting levels: control = 0, case = 1
Setting direction: controls < cases# Create a basic roc object
rocobj2019 <- roc(analysisData.GRS2019$Celiac ~ fit.GRS2019$fitted, ci = TRUE, plot=FALSE)Setting levels: control = 0, case = 1
Setting direction: controls < casesrocobj2019$ci # 95% CI: 0.6146-0.8517 (DeLong)95% CI: 0.6163-0.8563 (DeLong)ciobj2019 <- ci.se(rocobj2019, specificities=seq(0, 1, l=25))
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|============================================================================================================================================================| 100%dat.ci2019 <- data.frame(x = as.numeric(rownames(ciobj2019)),
lower = ciobj2019[, 1],
upper = ciobj2019[, 3])
theme_set(theme_gray(base_size = 12, base_family = "Arial") +
theme(panel.border = element_rect(colour="black", fill = "transparent"),
plot.title = element_text(face="bold", hjust = 0, size = 11), # lineheight=.8, size=20,
axis.text = element_text(color="black", size = 11),
axis.text.x = element_text(angle = 0, hjust = NULL),
strip.background = element_rect(colour="black", fill = "light grey", size = 1), # adjusts facet label borders (if any)
panel.background = element_blank(),
panel.grid = element_blank()
))
g2019 <- ggroc(rocobj2019,
alpha = 0.9,
ci = TRUE,
colour = RedBlue[[1]],
#colour = GrayBlue[[2]],
#linetype = 2,
size = 1,
legacy.axes = TRUE
) +
geom_segment(aes(x = 0, xend = 1, y = 0, yend = 1), color=GrayBlue[[2]]) + #, linetype="dashed")
coord_equal() +
labs(title = "Predictive accuracy of GRS* in DS",
subtitle = paste("Area under the curve: ", round(resRoc2019$auc, digits = 4), sep = ""),
caption = "Logistic: Celiac ~ GRS*\n*Sharp et al., 2019") +
theme(plot.caption = element_text(hjust = 0)) +
ylab("Sensitivity") +
xlab("1 - Specificity")Warning: Ignoring unknown parameters: cig2019
filename <- "MEGA_041822_ROC_GRS2019_vs_CeliacStatus_v0.1_JRS"
setwd(dir.GRSplots)Warning: The working directory was changed to /Users/shawjes/Dropbox/EspinosaGroup/ANALYSIS/Celiac_MultiOmics/GRS/DSMIG_Shared/Manuscript_Figure1/Plots inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.ggsave(filename = paste(filename, ".png", sep = ""), width = 3.5, height = 3.5, units = "in")setwd(dir.GRSplots)
ggsave(filename = paste(filename, ".pdf", sep = ""), device = cairo_pdf, width = 3.5, height = 3.5, units = "in")
For revised GRS (2022)
# AUC from pROC::roc()
library(pROC)
resRoc2022 <- roc(analysisData.GRS2022$Celiac ~ fit.GRS2022$fitted)Setting levels: control = 0, case = 1
Setting direction: controls < cases# Create a basic roc object
rocobj2022 <- roc(analysisData.GRS2022$Celiac ~ fit.GRS2022$fitted, ci = TRUE, plot=FALSE)Setting levels: control = 0, case = 1
Setting direction: controls < casesrocobj2022$ci # 95% CI: 0.6146-0.8517 (DeLong)95% CI: 0.6155-0.8553 (DeLong)ciobj2022 <- ci.se(rocobj2022, specificities=seq(0, 1, l=25))
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|============================================================================================================================================================| 100%dat.ci2022 <- data.frame(x = as.numeric(rownames(ciobj2022)),
lower = ciobj2022[, 1],
upper = ciobj2022[, 3])
theme_set(theme_gray(base_size = 12, base_family = "Arial") +
theme(panel.border = element_rect(colour="black", fill = "transparent"),
plot.title = element_text(face="bold", hjust = 0, size = 11), # lineheight=.8, size=20,
axis.text = element_text(color="black", size = 11),
axis.text.x = element_text(angle = 0, hjust = NULL),
strip.background = element_rect(colour="black", fill = "light grey", size = 1), # adjusts facet label borders (if any)
panel.background = element_blank(),
panel.grid = element_blank()
))
g2022 <- ggroc(rocobj2022,
alpha = 0.9,
ci = TRUE,
colour = RedBlue[[1]],
#colour = GrayBlue[[2]],
#linetype = 2,
size = 1,
legacy.axes = TRUE
) +
geom_segment(aes(x = 0, xend = 1, y = 0, yend = 1), color=GrayBlue[[2]]) + #, linetype="dashed")
coord_equal() +
labs(title = "Predictive accuracy of GRS* in DS",
subtitle = paste("Area under the curve: ", round(resRoc2022$auc, digits = 4), sep = ""),
caption = "Logistic: Celiac ~ GRS*\n*Sharp et al., 2022") +
theme(plot.caption = element_text(hjust = 0)) +
ylab("Sensitivity") +
xlab("1 - Specificity")Warning: Ignoring unknown parameters: cig2022
filename <- "MEGA_041822_ROC_GRS2022_vs_CeliacStatus_v0.1_JRS"
setwd(dir.GRSplots)Warning: The working directory was changed to /Users/shawjes/Dropbox/EspinosaGroup/ANALYSIS/Celiac_MultiOmics/GRS/DSMIG_Shared/Manuscript_Figure1/Plots inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.ggsave(filename = paste(filename, ".png", sep = ""), width = 3.5, height = 3.5, units = "in")setwd(dir.GRSplots)
ggsave(filename = paste(filename, ".pdf", sep = ""), device = cairo_pdf, width = 3.5, height = 3.5, units = "in")
Split the dataset to facilitate analysis of each variant with a separate logistic regression:
analysisData.GRSvariants2019.split <- analysisData.GRSvariants2019 %>%
mutate(`Variant (Sharp 2019)` = ifelse(grepl("DQ", `Variant (Sharp 2019)`)==TRUE, `Variant (Sharp 2019)`,
paste(`Variant (Sharp 2019)`, " (", Score_Allele, ")", sep = ""))) %>%
split(., .$`Variant (Sharp 2019)`)
analysisData.GRSvariants2022.split <- analysisData.GRSvariants2022 %>%
mutate(`Variant (Sharp 2022)` = ifelse(grepl("DQ", `Variant (Sharp 2022)`)==TRUE, `Variant (Sharp 2022)`,
paste(`Variant (Sharp 2022)`, " (", Score_Allele, ")", sep = ""))) %>%
split(., .$`Variant (Sharp 2022)`)
analysisData.GRSvariants2019.split %>% tail(n=2)$`rs990171 (A)`
$`X/X ()`analysisData.GRSvariants2022.split %>% tail(n=2)$`rs990171 (A)`
$`X/X ()`analysisData.GRSvariants2019.split %>% names() [1] "DQ2.2/DQ8" "DQ2.2/X" "DQ2.5/DQ2.2" "DQ2.5/DQ8" "DQ2.5/X" "DQ7.5/DQ2.2" "DQ7.5/DQ2.5" "DQ7.5/DQ8" "DQ7.5/X"
[10] "DQ8/X" "rs10167650 (G)" "rs1018326 (C)" "rs1050976 (T)" "rs10800746 (T)" "rs10892258 (A)" "rs11851414 (C)" "rs11875687 (C)" "rs12068671 (C)"
[19] "rs12142280 (A)" "rs1250552 (G)" "rs13003464 (G)" "rs13132308 (G)" "rs1353248 (T)" "rs1359062 (C)" "rs1378938 (T)" "rs1611710 (T)" "rs17264332 (G)"
[28] "rs182429 (A)" "rs1893592 (C)" "rs1980422 (C)" "rs2030519 (G)" "rs2301226 (G)" "rs2387397 (G)" "rs2561288 (T)" "rs3128927 (T)" "rs3184504 (T)"
[37] "rs4143332 (C)" "rs4445406 (C)" "rs4821124 (C)" "rs55743914 (T)" "rs61579022 (A)" "rs61907765 (T)" "rs6498114 (G)" "rs6715106 (G)" "rs6937061 (G)"
[46] "rs7104791 (T)" "rs7616215 (C)" "rs990171 (A)" "X/X ()" analysisData.GRSvariants2022.split %>% names() [1] "DQ2.2/DQ8" "DQ2.2/X" "DQ2.5/DQ2.2" "DQ2.5/DQ8" "DQ2.5/X" "DQ7.5/DQ2.2" "DQ7.5/DQ2.5" "DQ7.5/DQ8" "DQ7.5/X"
[10] "DQ8/X" "rs10167650 (G)" "rs1018326 (C)" "rs1050976 (T)" "rs10800746 (T)" "rs10892258 (A)" "rs11801183 (T)" "rs11851414 (C)" "rs11875687 (C)"
[19] "rs12068671 (C)" "rs1250552 (G)" "rs13003464 (G)" "rs13132308 (G)" "rs1323292 (G)" "rs1353248 (T)" "rs1378938 (T)" "rs1611710 (T)" "rs17264332 (G)"
[28] "rs182429 (A)" "rs1893592 (C)" "rs1980422 (C)" "rs2030519 (G)" "rs2301226 (G)" "rs2561288 (T)" "rs3128927 (T)" "rs3184504 (T)" "rs4143332 (G)"
[37] "rs4445406 (C)" "rs4821124 (C)" "rs55743914 (T)" "rs61579022 (A)" "rs61907765 (T)" "rs6498114 (G)" "rs6715106 (G)" "rs6937061 (G)" "rs7104791 (T)"
[46] "rs7616215 (C)" "rs947474 (G)" "rs990171 (A)" "X/X ()" Fit an additive genetic model for variant dosage with adjustment for the top 5 PCs for ancestry
Create a results table for each version of the GRS
results.variants.GRS2019 <- map2(.x = fit.variants.GRS2019,
.y = analysisData.GRSvariants2019.split,
.f = ~tidy(x = .x, conf.int=TRUE, data = .y)) %>%
bind_rows(.id="Variant") %>%
mutate(Model_Call= 'glm(Celiac ~ Dosage_for_GRS + PC1 + PC2 + PC3 + PC4 + PC5, family = binomial(link = "logit"), data = .)') %>%
select(Model_Call, everything()) %>%
filter(term == "Dosage_for_GRS") %>%
#group_by(term) %>% # Group by term if you haven't already limited your results dataframe to the variable for which you want to calculate adjusted P-values
mutate(Q = p.adjust(p.value, method = "BH")) %>%
#ungroup() %>%
mutate(GRS_version = analysisData.GRSvariants2022$GRS_version[1],
GRS_reference = analysisData.GRSvariants2022$GRS_reference[1]) %>%
select(GRS_version, GRS_reference, everything()) %>%
mutate(Variant = ifelse(Variant == "X/X ()", "X/X", Variant)) %>%
rename(logOR = estimate,
P = p.value) %>%
select(-c(std.error, statistic))Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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Warning: glm.fit: fitted probabilities numerically 0 or 1 occurredresults.variants.GRS2022 <- map2(.x = fit.variants.GRS2022,
.y = analysisData.GRSvariants2022.split,
.f = ~tidy(x = .x, conf.int=TRUE, data = .y)) %>%
bind_rows(.id="Variant") %>%
mutate(Model_Call= 'glm(Celiac ~ Dosage_for_GRS + PC1 + PC2 + PC3 + PC4 + PC5, family = binomial(link = "logit"), data = .)') %>%
select(Model_Call, everything()) %>%
filter(term == "Dosage_for_GRS") %>%
#group_by(term) %>% # Group by term first if you haven't already limited your results dataframe to the variable for which you want to calculate adjusted P-values
mutate(Q = p.adjust(p.value, method = "BH")) %>%
#ungroup() %>%
mutate(GRS_version = analysisData.GRSvariants2022$GRS_version[1],
GRS_reference = analysisData.GRSvariants2022$GRS_reference[1]) %>%
select(GRS_version, GRS_reference, everything()) %>%
mutate(Variant = ifelse(Variant == "X/X ()", "X/X", Variant)) %>%
rename(logOR = estimate,
P = p.value) %>%
select(-c(std.error, statistic))Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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Warning: glm.fit: fitted probabilities numerically 0 or 1 occurredView and output the results table for each version of the GRS
results.variants.GRS2019 %>% arrange(P)results.variants.GRS2022 %>% arrange(P)
setwd(dir.Results)Warning: The working directory was changed to /Users/shawjes/Dropbox/EspinosaGroup/ANALYSIS/Celiac_MultiOmics/GRS/DSMIG_Shared/Manuscript_Figure1/Results inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.fwrite(results.variants.GRS2019, "MEGA_041822_RESULTS_CDGRS2019_VariantDosage_vs_Celiac_v0.1_JRS.csv")
fwrite(results.variants.GRS2022, "MEGA_041822_RESULTS_CDGRS2022_VariantDosage_vs_Celiac_v0.1_JRS.csv")Volcano plots
GRS 2019
# results.variants.GRS2019
# Setting and modifying theme for plots
theme_set(theme_gray(base_size = 12, base_family = "Arial") +
theme(panel.border = element_rect(colour="black", fill = "transparent"),
plot.title = element_text(face="bold", hjust = 0), # lineheight=.8, size=20,
axis.text = element_text(color="black", size = 14),
axis.text.x = element_text(angle = 0, hjust = NULL),
strip.background = element_rect(colour="black", fill = "light grey", size = 1), # adjusts facet label borders (if any)
panel.background = element_blank(),
panel.grid = element_blank()
))
#
standard_colors <- c("#333333", "#009b4e")
summary(results.variants.GRS2019$logOR) Min. 1st Qu. Median Mean 3rd Qu. Max.
-16.21998 -0.27000 -0.03955 -0.27284 0.30717 1.76509 # Min. 1st Qu. Median Mean 3rd Qu. Max.
#-16.193 0.000 0.000 -0.270 0.000 1.471
results.variants.GRS2019 %>%
mutate(Q.Result_Category = ifelse(Q<0.10 & logOR>0, "Significantly up in DS with CD",
ifelse(Q<0.10 & logOR<0, "Significantly down in DS with CD", "Not statistically significant")),
P.Result_Category = ifelse(P<0.10 & logOR>0, "Significantly up in DS with CD",
ifelse(P<0.10 & logOR<0, "Significantly down in DS with CD", "Not statistically significant"))) %>%
arrange(desc(abs(logOR))) %>%
mutate(rank_logOR = row_number()) %>%
arrange(P) %>%
mutate(rank_P = row_number()) %>%
mutate(label = ifelse(rank_logOR<5 | rank_P<5, Variant, "")) %>%
ggplot(aes(logOR, -log10(Q))) +
geom_hline(yintercept = -log10(0.1), linetype = 2) +
geom_vline(xintercept = 0, linetype = 2) +
#geom_point(
# aes(color = "All")
# ) +
geom_point(data = . %>% filter(Q.Result_Category=="Not statistically significant"),
aes(), color = "dark gray") +
geom_point(data = . %>% filter(Q.Result_Category=="Significantly up in DS with CD"),
aes(), colour = "#153152") +
geom_point(data = . %>% filter(Q.Result_Category=="Significantly down in DS with CD"),
aes(), colour = "#006c9e") +
xlim(-20, 20) +
ylim(0,2) +
# set colors
xlab(bquote(''~log(OR)~'')) +
ylab(bquote(''~-log[10](Q)~'')) +
#ylab(bquote('-log10'~P[BH]~)) +
scale_color_manual(values = c("Not statistically significant" = "grey",
"Significantly up in DS with CD" = "#153152",
"Significantly down in DS with CD" = "#006c9e"), name = NULL) +
# Add gene labels
geom_text_repel(aes(label = label)) + #, xlim = c(2,4)) +
theme(aspect.ratio = 1.2,
#legend.position = c(0.15, 0.9), # depends on data scales
legend.position = "none" ) +
# legend.background = element_blank() ) +
labs(title = "Variant association with Celiac in DS",
caption = results.variants.GRS2019$Model_Call[1])Warning: ggrepel: 1 unlabeled data points (too many overlaps). Consider increasing max.overlapssetwd(dir.GRSplots)Warning: The working directory was changed to /Users/shawjes/Dropbox/EspinosaGroup/ANALYSIS/Celiac_MultiOmics/GRS/DSMIG_Shared/Manuscript_Figure1/Plots inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.filename <- "MEGA_041822_VolcanoPlot_GRS2019_Celiac_vs_Dosage_adj5PCs_displayQvals_v0.1_JRS"
ggsave(filename = paste(filename, ".png", sep = ""),
width = 5, height = 5, units = "in")Warning: ggrepel: 1 unlabeled data points (too many overlaps). Consider increasing max.overlapsggsave(filename = paste(filename, ".pdf", sep = ""),
device = cairo_pdf, width = 5, height = 5, units = "in")Warning: ggrepel: 1 unlabeled data points (too many overlaps). Consider increasing max.overlaps
results.variants.GRS2019 %>%
mutate(Q.Result_Category = ifelse(Q<0.10 & logOR>0, "Significantly up in DS with CD",
ifelse(Q<0.10 & logOR<0, "Significantly down in DS with CD", "Not statistically significant")),
P.Result_Category = ifelse(P<0.10 & logOR>0, "Significantly up in DS with CD",
ifelse(P<0.10 & logOR<0, "Significantly down in DS with CD", "Not statistically significant"))) %>%
arrange(desc(abs(logOR))) %>%
mutate(rank_logOR = row_number()) %>%
arrange(P) %>%
mutate(rank_P = row_number()) %>%
mutate(label = ifelse(rank_logOR<5 | rank_P<5, Variant, "")) %>%
ggplot(aes(logOR, -log10(P))) +
geom_hline(yintercept = -log10(0.1), linetype = 2) +
geom_vline(xintercept = 0, linetype = 2) +
#geom_point(
# aes(color = "All")
# ) +
geom_point(data = . %>% filter(Q.Result_Category=="Not statistically significant"),
aes(), color = "dark gray") +
geom_point(data = . %>% filter(Q.Result_Category=="Significantly up in DS with CD"),
aes(), colour = "#153152") +
geom_point(data = . %>% filter(Q.Result_Category=="Significantly down in DS with CD"),
aes(), colour = "#006c9e") +
xlim(-20, 20) +
ylim(0,2) +
# set colors
xlab(bquote(''~log(OR)~'')) +
ylab(bquote(''~-log[10](P)~'')) +
#ylab(bquote('-log10'~P[BH]~)) +
scale_color_manual(values = c("Not statistically significant" = "grey",
"Significantly up in DS with CD" = "#153152",
"Significantly down in DS with CD" = "#006c9e"), name = NULL) +
# Add gene labels
geom_text_repel(aes(label = label)) + #, xlim = c(2,4)) +
theme(aspect.ratio = 1.2,
#legend.position = c(0.15, 0.9), # depends on data scales
legend.position = "none" ) +
# legend.background = element_blank() ) +
labs(title = "Variant association with Celiac in DS",
caption = results.variants.GRS2019$Model_Call[1])
setwd(dir.GRSplots)
filename <- "MEGA_041822_VolcanoPlot_GRS2019_Celiac_vs_Dosage_adj5PCs_displayPvals_v0.1_JRS"
ggsave(filename = paste(filename, ".png", sep = ""),
width = 5, height = 5, units = "in")ggsave(filename = paste(filename, ".pdf", sep = ""),
device = cairo_pdf, width = 5, height = 5, units = "in")
GRS 2022
results.variants.GRS2022 %>%
mutate(Q.Result_Category = ifelse(Q<0.10 & logOR>0, "Significantly up in DS with CD",
ifelse(Q<0.10 & logOR<0, "Significantly down in DS with CD", "Not statistically significant")),
P.Result_Category = ifelse(P<0.10 & logOR>0, "Significantly up in DS with CD",
ifelse(P<0.10 & logOR<0, "Significantly down in DS with CD", "Not statistically significant"))) %>%
arrange(desc(abs(logOR))) %>%
mutate(rank_logOR = row_number()) %>%
arrange(P) %>%
mutate(rank_P = row_number()) %>%
mutate(label = ifelse(rank_logOR<5 | rank_P<5, Variant, "")) %>%
ggplot(aes(logOR, -log10(Q))) +
geom_hline(yintercept = -log10(0.1), linetype = 2) +
geom_vline(xintercept = 0, linetype = 2) +
#geom_point(
# aes(color = "All")
# ) +
geom_point(data = . %>% filter(Q.Result_Category=="Not statistically significant"),
aes(), color = "dark gray") +
geom_point(data = . %>% filter(Q.Result_Category=="Significantly up in DS with CD"),
aes(), colour = "#153152") +
geom_point(data = . %>% filter(Q.Result_Category=="Significantly down in DS with CD"),
aes(), colour = "#006c9e") +
xlim(-20, 20) +
ylim(0,2) +
# set colors
xlab(bquote(''~log(OR)~'')) +
ylab(bquote(''~-log[10](Q)~'')) +
#ylab(bquote('-log10'~P[BH]~)) +
scale_color_manual(values = c("Not statistically significant" = "grey",
"Significantly up in DS with CD" = "#153152",
"Significantly down in DS with CD" = "#006c9e"), name = NULL) +
# Add gene labels
geom_text_repel(aes(label = label)) + #, xlim = c(2,4)) +
theme(aspect.ratio = 1.2,
#legend.position = c(0.15, 0.9), # depends on data scales
legend.position = "none" ) +
# legend.background = element_blank() ) +
labs(title = "Variant association with Celiac in DS",
caption = results.variants.GRS2022$Model_Call[1])Warning: ggrepel: 5 unlabeled data points (too many overlaps). Consider increasing max.overlapssetwd(dir.GRSplots)Warning: The working directory was changed to /Users/shawjes/Dropbox/EspinosaGroup/ANALYSIS/Celiac_MultiOmics/GRS/DSMIG_Shared/Manuscript_Figure1/Plots inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.filename <- "MEGA_041822_VolcanoPlot_GRS2022_Celiac_vs_Dosage_adj5PCs_displayQvals_v0.1_JRS"
ggsave(filename = paste(filename, ".png", sep = ""),
width = 5, height = 5, units = "in")Warning: ggrepel: 1 unlabeled data points (too many overlaps). Consider increasing max.overlapsggsave(filename = paste(filename, ".pdf", sep = ""),
device = cairo_pdf, width = 5, height = 5, units = "in")Warning: ggrepel: 1 unlabeled data points (too many overlaps). Consider increasing max.overlaps
results.variants.GRS2022 %>%
mutate(Q.Result_Category = ifelse(Q<0.10 & logOR>0, "Significantly up in DS with CD",
ifelse(Q<0.10 & logOR<0, "Significantly down in DS with CD", "Not statistically significant")),
P.Result_Category = ifelse(P<0.10 & logOR>0, "Significantly up in DS with CD",
ifelse(P<0.10 & logOR<0, "Significantly down in DS with CD", "Not statistically significant"))) %>%
arrange(desc(abs(logOR))) %>%
mutate(rank_logOR = row_number()) %>%
arrange(P) %>%
mutate(rank_P = row_number()) %>%
mutate(label = ifelse(rank_logOR<5 | rank_P<5, Variant, "")) %>%
ggplot(aes(logOR, -log10(P))) +
geom_hline(yintercept = -log10(0.1), linetype = 2) +
geom_vline(xintercept = 0, linetype = 2) +
#geom_point(
# aes(color = "All")
# ) +
geom_point(data = . %>% filter(Q.Result_Category=="Not statistically significant"),
aes(), color = "dark gray") +
geom_point(data = . %>% filter(Q.Result_Category=="Significantly up in DS with CD"),
aes(), colour = "#153152") +
geom_point(data = . %>% filter(Q.Result_Category=="Significantly down in DS with CD"),
aes(), colour = "#006c9e") +
xlim(-20, 20) +
ylim(0,2) +
# set colors
xlab(bquote(''~log(OR)~'')) +
ylab(bquote(''~-log[10](P)~'')) +
#ylab(bquote('-log10'~P[BH]~)) +
scale_color_manual(values = c("Not statistically significant" = "grey",
"Significantly up in DS with CD" = "#153152",
"Significantly down in DS with CD" = "#006c9e"), name = NULL) +
# Add gene labels
geom_text_repel(aes(label = label)) + #, xlim = c(2,4)) +
theme(aspect.ratio = 1.2,
#legend.position = c(0.15, 0.9), # depends on data scales
legend.position = "none" ) +
# legend.background = element_blank() ) +
labs(title = "Variant association with Celiac in DS",
caption = results.variants.GRS2022$Model_Call[1])
setwd(dir.GRSplots)
filename <- "MEGA_041822_VolcanoPlot_GRS2022_Celiac_vs_Dosage_adj5PCs_displayPvals_v0.1_JRS"
ggsave(filename = paste(filename, ".png", sep = ""),
width = 5, height = 5, units = "in")ggsave(filename = paste(filename, ".pdf", sep = ""),
device = cairo_pdf, width = 5, height = 5, units = "in")
ARCHIVE
Read in supplemental tables from Sharp et al., 2019
setwd(dir)
Table_S2 <- read.xlsx("apt15826-sup-0001-supinfo.xlsx", sheet = "Table S2", startRow = 3) %>%
separate(`Odds.Ratio.[95%.CI]`, into = c("OR", "CI"), sep = " ", extra = "merge", remove = TRUE) %>%
rename(OR.GRS = OR,
CI.GRS = CI,
Weight.GRS = `Weight.(β)`)
Table_S2
setwd(dir)
Table_S3 <- read.xlsx("apt15826-sup-0001-supinfo.xlsx", sheet = "Table S3", startRow = 3) %>%
rename(OR.GRS = OR,
Weight.GRS = `Weight.(β)`)
Table_S3Check that we have the expected number of Celiac cases in the analysis dataset
analysisData %>%
select(RecordID, Celiac) %>%
unique() %>%
group_by(Celiac) %>%
summarise(N = n())
# Good.Check if all of our participants have complete dosage for every HLA-DQ genotype and every non-HLA SNP
Check for any samples with more than 1 LabID in the analysis data
Prepare dataframe for analysis of GRS (excluding variant-specific data)
analysisData.GRS <- analysisData %>%
select(-c(Variant, Dosage, MEGA.VariantID,
AT_GC_SNP, MEGA.CHR, MEGA.BP, Weight.GRS,)) %>%
unique()
analysisData.GRSRun logistic regression to test for association of GRS with Celiac status among T21s
set.seed(1234)
fit.GRS <- glm(Celiac ~ GRS,
family = binomial(link = "logit"),
data = analysisData.GRS)
results.GRS <- fit.GRS %>% tidy(conf.int = TRUE) %>%
select(-c(std.error, statistic)) %>%
rename(Term = term,
logOR = estimate,
conf.low_logOR = conf.low,
conf.high_logOR = conf.high,
P = p.value) %>%
mutate(OR = exp(logOR),
conf.low_OR = exp(conf.low_logOR),
conf.high_OR = exp(conf.high_logOR)) %>%
select(-c(logOR, conf.low_logOR, conf.high_logOR)) %>%
mutate(`95% CI` = paste("(", round(conf.low_OR, digits = 2), ", ", round(conf.high_OR, digits = 2), ")", sep = "")) %>%
mutate(OR = round(OR, digits = 2),
P = round(P, digits = 4)) %>%
filter(Term == "GRS") %>%
mutate(Term = gsub("GRS", "GRS*", Term)) %>%
select(Term, OR, `95% CI`, P)
results.GRSRun logistic regression to test for association of Celiac with the non-HLA-DQ component of the GRS
set.seed(1234)
fit.GRS_2components <- glm(Celiac ~ GRS.HLADQ_component + GRS.nonHLADQ_component,
family = binomial(link = "logit"),
data = analysisData.GRS)
results.GRS_2components <- fit.GRS_2components %>% tidy(conf.int = TRUE) %>%
select(-c(std.error, statistic)) %>%
rename(Term = term,
logOR = estimate,
conf.low_logOR = conf.low,
conf.high_logOR = conf.high,
P = p.value) %>%
mutate(OR = exp(logOR),
conf.low_OR = exp(conf.low_logOR),
conf.high_OR = exp(conf.high_logOR)) %>%
select(-c(logOR, conf.low_logOR, conf.high_logOR)) %>%
mutate(`95% CI` = paste("(", round(conf.low_OR, digits = 2), ", ", round(conf.high_OR, digits = 2), ")", sep = "")) %>%
mutate(OR = round(OR, digits = 2),
P = round(P, digits = 4)) %>%
filter(Term == "GRS.HLADQ_component" | Term == "GRS.nonHLADQ_component") %>%
mutate(Term = gsub("GRS.nonHLADQ_component", "GRS*, non-HLA-DQ component", Term),
Term = gsub("GRS.HLADQ_component", "GRS*, HLA-DQ component", Term)) %>%
select(Term, OR, `95% CI`, P)
results.GRS_2componentsCreate output object to write to multi-sheet XLSX workbook
results.list <- list("GRS*" = results.GRS,
"GRS*, split components" = results.GRS_2components)Save results
setwd(dir.Results)
write.xlsx(results.list, "Celiac_022322_RESULTS_Logistic_modifiedGRS_vs_CeliacStatus_v0.1_JRS.xlsx", overwrite=TRUE)
setwd(dir.Results)
fwrite(results.GRS, "Celiac_022322_RESULTS_Logistic_modifiedGRS_vs_CeliacStatus_v0.1_JRS.csv")
fwrite(results.GRS, "Celiac_022322_RESULTS_Logistic_modifiedGRS_vs_CeliacStatus_v0.1_JRS.tsv", sep = "\t")
setwd(dir.Results)
fwrite(results.GRS_2components, "Celiac_022322_RESULTS_Logistic_modifiedGRSsplit_vs_CeliacStatus_v0.1_JRS.csv")
fwrite(results.GRS_2components, "Celiac_022322_RESULTS_Logistic_modifiedGRSsplit_vs_CeliacStatus_v0.1_JRS.tsv", sep = "\t")Plot ROC curves for GRS in DS
# AUC from pROC::roc()
library(pROC)
resRoc <- roc(analysisData.GRS$Celiac ~ fit.GRS_2components$fitted)
# Create a basic roc object
rocobj <- roc(analysisData.GRS$Celiac ~ fit.GRS_2components$fitted, ci = TRUE, plot=FALSE)
rocobj$ci # 95% CI: 0.6146-0.8517 (DeLong)
ciobj <- ci.se(rocobj, specificities=seq(0, 1, l=25))
dat.ci <- data.frame(x = as.numeric(rownames(ciobj)),
lower = ciobj[, 1],
upper = ciobj[, 3])
theme_set(theme_gray(base_size = 12, base_family = "Arial") +
theme(panel.border = element_rect(colour="black", fill = "transparent"),
plot.title = element_text(face="bold", hjust = 0, size = 11), # lineheight=.8, size=20,
axis.text = element_text(color="black", size = 11),
axis.text.x = element_text(angle = 0, hjust = NULL),
plot.caption = element_text(size = 8),
strip.background = element_rect(colour="black", fill = "light grey", size = 1), # adjusts facet label borders (if any)
panel.background = element_blank(),
panel.grid = element_blank()
))
g <- ggroc(rocobj,
alpha = 0.9,
ci = TRUE,
colour = RedBlue[[1]],
#colour = GrayBlue[[2]],
#linetype = 2,
size = 1,
legacy.axes = TRUE
) +
geom_segment(aes(x = 0, xend = 1, y = 0, yend = 1), color=GrayBlue[[2]]) + #, linetype="dashed")
coord_equal() +
labs(title = "Predictive accuracy of GRS* in DS",
subtitle = paste("Area under the curve: ", round(resRoc$auc, digits = 4), sep = ""),
caption = "Logistic: Celiac ~ GRS_HLADQ + GRS_nonHLADQ") +
theme(plot.caption = element_text(hjust = 0)) +
ylab("Sensitivity") +
xlab("1 - Specificity")
g
filename <- "MEGA_022322_ROC_modifiedGRS_splitComponents_vs_CeliacStatus_v0.1_JRS"
setwd(dir.Figures)
ggsave(filename = paste(filename, ".png", sep = ""), width = 3.5, height = 3.5, units = "in")
setwd(dir.Figures)
ggsave(filename = paste(filename, ".pdf", sep = ""), device = cairo_pdf, width = 3.5, height = 3.5, units = "in")---
title: "R Notebook"
output: html_notebook
---

#### Load frequently used packages
```{r}
library(skimr)
library(openxlsx)
library(ggrepel)
library(dplyr)
library(tidyr)
library(data.table)
library(broom)
library(broomExtra)
library(tibble)
library(sjstats)
library(car)
library(lme4)
library(lmerTest)
library(ggplot2)
library(tibble)
library(modelr)
library(tidyverse)
#library(miceadds)
library(ggforce)
require(openxlsx)
library(tidyverse)
library(caret)
library(glmnet)
library(ggplot2)
library(gridExtra)
library(MASS) # rlm
library(lmPerm)
library(circlize)
library(RColorBrewer)
library(ComplexHeatmap)

select <- dplyr::select
filter <- dplyr::filter
```

#### Define the directories used in this script
```{r}
dir.IlluminaProductFiles <- "/Users/shawjes/Dropbox/EspinosaGroup/DATA_MAIN/MEGA/chr21_genotyping/Illumina_ProductFiles"
dir.chr21_genotyping <- "/Users/shawjes/Dropbox/EspinosaGroup/DATA_MAIN/MEGA/chr21_genotyping"
dir.chr21_idatQuants <- "/Users/shawjes/Dropbox/EspinosaGroup/DATA_MAIN/MEGA/chr21_genotyping/idatQuants"
dir.GRSdata <- "/Users/shawjes/Dropbox/EspinosaGroup/ANALYSIS/Celiac_MultiOmics/GRS/DSMIG_Shared/Manuscript_Figure1/Data"
dir.GRSplots <- "/Users/shawjes/Dropbox/EspinosaGroup/ANALYSIS/Celiac_MultiOmics/GRS/DSMIG_Shared/Manuscript_Figure1/Plots"
dir.GRSoriginal.Anno <- "/Users/shawjes/Dropbox/EspinosaGroup/ANALYSIS/Celiac_MultiOmics/GRS/DSMIG_Shared/Manuscript_Figure1/Annotation/GRSoriginal"
dir.GRSrevised.Anno <- "/Users/shawjes/Dropbox/EspinosaGroup/ANALYSIS/Celiac_MultiOmics/GRS/DSMIG_Shared/Manuscript_Figure1/Annotation/GRSrevised"
dir.PCA <- '/Users/shawjes/Dropbox/EspinosaGroup/ANALYSIS/Celiac_MultiOmics/GRS/DSMIG_Shared/Manuscript_Figure1/Data/PCA'
dir.Results <- '/Users/shawjes/Dropbox/EspinosaGroup/ANALYSIS/Celiac_MultiOmics/GRS/DSMIG_Shared/Manuscript_Figure1/Results'
```

#### Print session info for reproducibility
```{r}
save.sessionInfo <- sessionInfo()

save.sessionInfo
```

#### Setting and modifying theme for plots
```{r}
theme_set(theme_gray(base_size = 12, base_family = "Arial") +
            theme(panel.border = element_rect(colour="black", fill = "transparent"),
                  plot.title = element_text(face="bold", hjust = 0), # lineheight=.8, size=20,
                  axis.text = element_text(color="black", size = 14),
                  axis.text.x = element_text(angle = 0, hjust = NULL),
                  strip.background = element_rect(colour="black", fill = "light grey", size = 1), # adjusts facet label borders (if any)
                  panel.background = element_blank(),
                  panel.grid = element_blank()
          ))
RedBlue <- c("#CD3333", "#1874CD")
GrayBlue <- c("grey", "#2b8cbe")
```

#### Initialize an object to store the column names for the identifiers we want to keep in each analysis dataframe
```{r}
ID_colnames <- c("FamilyID", "RecordID", "MEGA.FID", "MEGA.IID", "MEGA.LabID")
```

#### Read in prepared metadata
```{r}
setwd(dir.GRSdata)
analysis_meta <- fread("MEGA_041822_META_CeliacGRS_v0.1_JRS.csv") %>%
  select(ID_colnames, Celiac, LabID, Celiac, EXCLUDE_from_analysis, EXCLUDE_reason) %>%
  unique()

analysis_meta
```

#### Read in prepared analysis datasets and keep only the IDs included in analysis
```{r}
setwd(dir.GRSdata)
analysisData.GRSorig <- fread("MEGA_041822_AnalysisData_CDGRS_Sharp2019_v0.1_JRS.csv") %>%
  left_join(analysis_meta, by = ID_colnames) %>%
  filter(EXCLUDE_from_analysis == 0)

analysisData.GRSrevised <- fread("MEGA_041822_AnalysisData_CDGRS_Sharp2022_v0.1_JRS.csv") %>%
  left_join(analysis_meta, by = ID_colnames) %>%
  filter(EXCLUDE_from_analysis == 0)

analysisData.GRSorig
analysisData.GRSrevised
```

#### Check that AFTER applying the EXCLUDE_from_analysis filter, unique IIDs x unique variants = nrows
```{r}
analysisData.GRSorig %>% nrow()
(analysisData.GRSorig %>% select(`Variant (Sharp 2019)`) %>% unique() %>% nrow())*(analysisData.GRSorig %>% select(RecordID) %>% unique() %>% nrow())

analysisData.GRSrevised %>% nrow()
(analysisData.GRSrevised %>% select(`Variant (Sharp 2022)`) %>% unique() %>% nrow())*(analysisData.GRSrevised %>% select(RecordID) %>% unique() %>% nrow())
```

#### Read in prepared ancestry PCs
```{r}
setwd(dir.PCA)
ancestry_PCs <- fread("MEGA_041822_Espinosa_MEGA2_HTP_GS_08132019_updated_callrate_passing_QC_KEEPforHLAvsCeliac_EXCLUDEvariants_mind05_geno0.02_maf0.05_PRUNEDindeppairwise0.2_v0.1_JRS.eigenvec") %>%
  select(FID, IID, PC1, PC2, PC3, PC4, PC5) %>%
  rename(MEGA.FID = FID, MEGA.IID = IID)

ancestry_PCs
```

#### Add PCs for ancestry and prepare the dataframes for analysis of GRS vs. Celiac status
```{r}
analysisData.GRS2019 <- analysisData.GRSorig %>%
  select(GRS_version, GRS_reference,
         ID_colnames, Celiac,
         GRS) %>%
  unique() %>%
  left_join(ancestry_PCs, by = c("MEGA.FID", "MEGA.IID"))

analysisData.GRS2022 <- analysisData.GRSrevised %>%
  select(GRS_version, GRS_reference,
         ID_colnames, Celiac,
         GRS) %>%
  unique() %>%
  left_join(ancestry_PCs, by = c("MEGA.FID", "MEGA.IID"))

analysisData.GRS2019
analysisData.GRS2022
```

#### Prepare dataframes for analysis of individual GRS variants vs. Celiac status
```{r}
analysisData.GRSvariants2019 <- analysisData.GRSorig %>%
  select(GRS_version, GRS_reference,
         ID_colnames, Celiac,
         `Variant (Sharp 2019)`, Score_Allele, Dosage_for_GRS,
         `Putative Gene (Sharp 2019)`) %>%
  unique() %>%
  left_join(ancestry_PCs, by = c("MEGA.FID", "MEGA.IID"))

analysisData.GRSvariants2022 <- analysisData.GRSrevised %>%
  select(GRS_version, GRS_reference,
         ID_colnames, Celiac,
         `Variant (Sharp 2022)`, Score_Allele, Dosage_for_GRS,
         `Putative Gene (Sharp 2019)`) %>%
  unique() %>%
  left_join(ancestry_PCs, by = c("MEGA.FID", "MEGA.IID"))

analysisData.GRSvariants2019
analysisData.GRSvariants2022

# Check that nrows still makes sense
analysisData.GRSvariants2019 %>% nrow()
(analysisData.GRSvariants2019 %>% select(`Variant (Sharp 2019)`) %>% unique() %>% nrow())*(analysisData.GRSvariants2019 %>% select(RecordID) %>% unique() %>% nrow())

analysisData.GRSvariants2022 %>% nrow()
(analysisData.GRSvariants2022 %>% select(`Variant (Sharp 2022)`) %>% unique() %>% nrow())*(analysisData.GRSvariants2022 %>% select(RecordID) %>% unique() %>% nrow())
```

#### Check that we have the expected number of Celiac cases and controls in each analysis dataset
```{r}
analysisData.GRS2019 %>%
  select(RecordID, Celiac) %>%
  unique() %>%
  group_by(Celiac) %>%
  summarise(N = n())

analysisData.GRS2022 %>%
  select(RecordID, Celiac) %>%
  unique() %>%
  group_by(Celiac) %>%
  summarise(N = n())

analysisData.GRSvariants2019 %>%
  select(RecordID, Celiac) %>%
  unique() %>%
  group_by(Celiac) %>%
  summarise(N = n())

analysisData.GRSvariants2022 %>%
  select(RecordID, Celiac) %>%
  unique() %>%
  group_by(Celiac) %>%
  summarise(N = n())

# Good.
```

##### Check that # variants * # individuals = # rows
```{r}
length(unique(analysisData.GRSvariants2019$MEGA.IID))
length(unique(analysisData.GRSvariants2019$`Variant (Sharp 2019)`))
identical(nrow(analysisData.GRSvariants2019),
          length(unique(analysisData.GRSvariants2019$MEGA.IID))*length(unique(analysisData.GRSvariants2019$`Variant (Sharp 2019)`)))

length(unique(analysisData.GRSvariants2022$MEGA.IID))
length(unique(analysisData.GRSvariants2022$`Variant (Sharp 2022)`))
identical(nrow(analysisData.GRSvariants2022),
          length(unique(analysisData.GRSvariants2022$MEGA.IID))*length(unique(analysisData.GRSvariants2022$`Variant (Sharp 2022)`)))
```

#### Verify that the two versions of GRS are not identical
Although the score weights do not change from GRS 2019 to GRS 2022, the dosage values for SNPs that were replaced are not identical. Therefore, the GRS calculated from the 2019 SNP list should be slightly different from the GRS calculated from the 2022 SNP list.
```{r}
analysisData.GRS2019 %>%
  select(MEGA.IID, GRS) %>%
  unique() %>%
  rename(GRS_2019 = GRS) %>%
  full_join(analysisData.GRS2022 %>%
              select(MEGA.IID, GRS) %>%
              unique() %>%
              rename(GRS_2022 = GRS),
            by = "MEGA.IID") %>%
  mutate(abs_Delta_GRS = abs(GRS_2022 - GRS_2019)) %>%
  arrange(desc(abs_Delta_GRS))

# Good.
```

#### View the four analysis dataframes to be used as input to logistic regression models
```{r}
analysisData.GRS2019

analysisData.GRS2022

analysisData.GRSvariants2019

analysisData.GRSvariants2022
```

#### Run logistic regression to test for association of GRS with Celiac status among T21s
```{r}
set.seed(1234)
fit.GRS2019 <- glm(Celiac ~ GRS,
               family = binomial(link = "logit"),
               data = analysisData.GRS2019)

set.seed(1234)
fit.GRS2022 <- glm(Celiac ~ GRS,
               family = binomial(link = "logit"),
               data = analysisData.GRS2022)

results.GRS2019 <- fit.GRS2019  %>% tidy(conf.int = TRUE) %>%
  select(-c(std.error, statistic)) %>%
  rename(Term = term,
         logOR = estimate,
         conf.low_logOR = conf.low,
         conf.high_logOR = conf.high,
         P = p.value) %>%
  mutate(OR = exp(logOR),
         conf.low_OR = exp(conf.low_logOR),
         conf.high_OR = exp(conf.high_logOR)) %>%
  #select(-c(logOR, conf.low_logOR, conf.high_logOR)) %>%
  mutate(`95% CI` = paste("(", round(conf.low_OR, digits = 2), ", ", round(conf.high_OR, digits = 2), ")", sep = "")) %>%
  mutate(OR = round(OR, digits = 2),
         P = round(P, digits = 4)) %>%
  filter(Term == "GRS") %>%
  mutate(Term = gsub("GRS", "GRS*", Term)) %>%
  select(Term, logOR, OR, `95% CI`, P)

results.GRS2022 <- fit.GRS2022 %>% tidy(conf.int = TRUE) %>%
  select(-c(std.error, statistic)) %>%
  rename(Term = term,
         logOR = estimate, # The 'estimate' column from a logistic regression represents the logOR
         conf.low_logOR = conf.low,
         conf.high_logOR = conf.high,
         P = p.value) %>%
  mutate(OR = exp(logOR), # OR = e^OR = exp(OR)
         conf.low_OR = exp(conf.low_logOR),
         conf.high_OR = exp(conf.high_logOR)) %>%
  #select(-c(logOR conf.low_logOR, conf.high_logOR)) %>%
  mutate(`95% CI` = paste("(", round(conf.low_OR, digits = 2), ", ", round(conf.high_OR, digits = 2), ")", sep = "")) %>%
  mutate(OR = round(OR, digits = 2),
         P = round(P, digits = 4)) %>%
  filter(Term == "GRS") %>%
  mutate(Term = gsub("GRS", "GRS*", Term)) %>%
  select(Term, logOR, OR, `95% CI`, P)

results.GRS2019
results.GRS2022

setwd(dir.Results)
fwrite(results.GRS2019, "MEGA_041822_RESULTS_CDGRS2019_GRS_vs_Celiac_v0.1_JRS.csv")
fwrite(results.GRS2022, "MEGA_041822_RESULTS_CDGRS2022_GRS_vs_Celiac_v0.1_JRS.csv")
```

#### Set theme and colors for plots
```{r}
theme_set(theme_gray(base_size = 12, base_family = "Arial") +
            theme(panel.border = element_rect(colour="black", fill = "transparent"),
                  plot.title = element_text(face="bold", hjust = 0), # lineheight=.8, size=20,
                  axis.text = element_text(color="black", size = 11),
                  axis.text.x = element_text(angle = 0, hjust = NULL),
                  strip.background = element_rect(colour="black", fill = "light grey", size = 1), # adjusts facet label borders (if any)
                  panel.background = element_blank(),
                  panel.grid = element_blank()
          ))

GrayBlue <- c("grey", "#2b8cbe")
RedBlue <- c("#CD3333", "#1874CD")
```

#### Plot the distribution of modified GRS among T21s with vs. without Celiac
```{r}
theme_set(theme_gray(base_size = 12, base_family = "Arial") +
            theme(panel.border = element_rect(colour="black", fill = "transparent"),
                  plot.title = element_text(face="bold", hjust = 0, size = 12), # lineheight=.8, size=20,
                  axis.text = element_text(color="black", size = 11),
                  axis.text.x = element_text(angle = 0, hjust = NULL),
                  strip.background = element_rect(colour="black", fill = "light grey", size = 1), # adjusts facet label borders (if any)
                  panel.background = element_blank(),
                  panel.grid = element_blank()
          ))

analysisData.GRS2019$GRS %>% summary() # xlim(-1, 6.5)
#   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#  -0.653   1.016   2.055   2.437   3.894   6.490 
analysisData.GRS2022$GRS %>% summary() # xlim(-1, 6.5)
#   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#  -0.653   1.016   2.050   2.449   3.966   6.490 
  
analysisData.GRS2019 %>%
  mutate(Group = ifelse(Celiac == 0, "DS without CD", ifelse(Celiac == 1, "DS with CD", NA)),
         Group = factor(Group, levels = c("DS without CD", "DS with CD")))  %>%
  ggplot(aes(x = GRS, y = ..scaled.., color = Group, fill = Group)) +
  geom_density(alpha = 0.5) +
  scale_colour_manual(values = GrayBlue) +
  scale_fill_manual(values = GrayBlue) + 
  theme(legend.position = "bottom",
        legend.title = element_blank(),
        plot.caption = element_text(hjust = 0),
        aspect.ratio = 1) +
  xlab("GRS*") +
  labs(title = "Celiac GRS* in DS +/- CD",
       subtitle = paste("OR = ", results.GRS2019$OR[1], " ", results.GRS2019$`95% CI`[1], "\nP<", results.GRS2019$P[1], sep = ""),
       caption = "*Sharp et al., 2019")

filename <- "MEGA_041822_DensityPlot_GRS2019_by_CeliacStatus_v0.1_JRS"
setwd(dir.GRSplots)
ggsave(filename = paste(filename, ".png", sep = ""), width = 3.6, height = 3.5, units = "in")
setwd(dir.GRSplots)
ggsave(filename = paste(filename, ".pdf", sep = ""), device = cairo_pdf, width = 3.6, height = 3.5, units = "in")

analysisData.GRS2022 %>%
  mutate(Group = ifelse(Celiac == 0, "DS without CD", ifelse(Celiac == 1, "DS with CD", NA)),
         Group = factor(Group, levels = c("DS without CD", "DS with CD")))  %>%
  ggplot(aes(x = GRS, y = ..scaled.., color = Group, fill = Group)) +
  geom_density(alpha = 0.5) +
  scale_colour_manual(values = GrayBlue) +
  scale_fill_manual(values = GrayBlue) + 
  theme(legend.position = "bottom",
        legend.title = element_blank(),
        plot.caption = element_text(hjust = 0),
        aspect.ratio = 1) +
  xlab("GRS*") +
  labs(title = "Celiac GRS* in DS +/- CD",
       subtitle = paste("OR = ", results.GRS2022$OR[1], " ", results.GRS2022$`95% CI`[1], "\nP<", results.GRS2022$P[1], sep = ""),
       caption = "*Sharp et al., 2022")

filename <- "MEGA_041822_DensityPlot_GRS2022_by_CeliacStatus_v0.1_JRS"
setwd(dir.GRSplots)
ggsave(filename = paste(filename, ".png", sep = ""), width = 3.6, height = 3.5, units = "in")
setwd(dir.GRSplots)
ggsave(filename = paste(filename, ".pdf", sep = ""), device = cairo_pdf, width = 3.6, height = 3.5, units = "in")
```

#### Plot ROC curves for GRS in DS

#### For original GRS (2019)
```{r}
# AUC from pROC::roc()
library(pROC)

resRoc2019 <- roc(analysisData.GRS2019$Celiac ~ fit.GRS2019$fitted)

# Create a basic roc object
rocobj2019 <- roc(analysisData.GRS2019$Celiac ~ fit.GRS2019$fitted, ci = TRUE, plot=FALSE)
rocobj2019$ci # 95% CI: 0.6146-0.8517 (DeLong)
ciobj2019 <- ci.se(rocobj2019, specificities=seq(0, 1, l=25))
dat.ci2019 <- data.frame(x = as.numeric(rownames(ciobj2019)),
                     lower = ciobj2019[, 1],
                     upper = ciobj2019[, 3])

theme_set(theme_gray(base_size = 12, base_family = "Arial") +
            theme(panel.border = element_rect(colour="black", fill = "transparent"),
                  plot.title = element_text(face="bold", hjust = 0, size = 11), # lineheight=.8, size=20,
                  axis.text = element_text(color="black", size = 11),
                  axis.text.x = element_text(angle = 0, hjust = NULL),
                  strip.background = element_rect(colour="black", fill = "light grey", size = 1), # adjusts facet label borders (if any)
                  panel.background = element_blank(),
                  panel.grid = element_blank()
          ))
g2019 <- ggroc(rocobj2019,
           alpha = 0.9,
           ci = TRUE,
           colour = RedBlue[[1]],
           #colour = GrayBlue[[2]],
           #linetype = 2,
           size = 1,
           legacy.axes = TRUE
           ) +
  geom_segment(aes(x = 0, xend = 1, y = 0, yend = 1), color=GrayBlue[[2]]) + #, linetype="dashed")
  coord_equal() +
  labs(title = "Predictive accuracy of GRS* in DS",
       subtitle = paste("Area under the curve: ", round(resRoc2019$auc, digits = 4), sep = ""),
       caption = "Logistic: Celiac ~ GRS*\n*Sharp et al., 2019") +
  theme(plot.caption = element_text(hjust = 0)) +
  ylab("Sensitivity") +
  xlab("1 - Specificity")
g2019

filename <- "MEGA_041822_ROC_GRS2019_vs_CeliacStatus_v0.1_JRS"
setwd(dir.GRSplots)
ggsave(filename = paste(filename, ".png", sep = ""), width = 3.5, height = 3.5, units = "in")
setwd(dir.GRSplots)
ggsave(filename = paste(filename, ".pdf", sep = ""), device = cairo_pdf, width = 3.5, height = 3.5, units = "in")
```

#### For revised GRS (2022)
```{r}
# AUC from pROC::roc()
library(pROC)

resRoc2022 <- roc(analysisData.GRS2022$Celiac ~ fit.GRS2022$fitted)

# Create a basic roc object
rocobj2022 <- roc(analysisData.GRS2022$Celiac ~ fit.GRS2022$fitted, ci = TRUE, plot=FALSE)
rocobj2022$ci # 95% CI: 0.6146-0.8517 (DeLong)
ciobj2022 <- ci.se(rocobj2022, specificities=seq(0, 1, l=25))
dat.ci2022 <- data.frame(x = as.numeric(rownames(ciobj2022)),
                     lower = ciobj2022[, 1],
                     upper = ciobj2022[, 3])

theme_set(theme_gray(base_size = 12, base_family = "Arial") +
            theme(panel.border = element_rect(colour="black", fill = "transparent"),
                  plot.title = element_text(face="bold", hjust = 0, size = 11), # lineheight=.8, size=20,
                  axis.text = element_text(color="black", size = 11),
                  axis.text.x = element_text(angle = 0, hjust = NULL),
                  strip.background = element_rect(colour="black", fill = "light grey", size = 1), # adjusts facet label borders (if any)
                  panel.background = element_blank(),
                  panel.grid = element_blank()
          ))
g2022 <- ggroc(rocobj2022,
           alpha = 0.9,
           ci = TRUE,
           colour = RedBlue[[1]],
           #colour = GrayBlue[[2]],
           #linetype = 2,
           size = 1,
           legacy.axes = TRUE
           ) +
  geom_segment(aes(x = 0, xend = 1, y = 0, yend = 1), color=GrayBlue[[2]]) + #, linetype="dashed")
  coord_equal() +
  labs(title = "Predictive accuracy of GRS* in DS",
       subtitle = paste("Area under the curve: ", round(resRoc2022$auc, digits = 4), sep = ""),
       caption = "Logistic: Celiac ~ GRS*\n*Sharp et al., 2022") +
  theme(plot.caption = element_text(hjust = 0)) +
  ylab("Sensitivity") +
  xlab("1 - Specificity")
g2022

filename <- "MEGA_041822_ROC_GRS2022_vs_CeliacStatus_v0.1_JRS"
setwd(dir.GRSplots)
ggsave(filename = paste(filename, ".png", sep = ""), width = 3.5, height = 3.5, units = "in")
setwd(dir.GRSplots)
ggsave(filename = paste(filename, ".pdf", sep = ""), device = cairo_pdf, width = 3.5, height = 3.5, units = "in")
```


#### Split the dataset to facilitate analysis of each variant with a separate logistic regression:
```{r}
analysisData.GRSvariants2019.split <- analysisData.GRSvariants2019 %>%
  mutate(`Variant (Sharp 2019)` = ifelse(grepl("DQ", `Variant (Sharp 2019)`)==TRUE, `Variant (Sharp 2019)`,
                                         paste(`Variant (Sharp 2019)`, " (", Score_Allele, ")", sep = ""))) %>%
  split(., .$`Variant (Sharp 2019)`)

analysisData.GRSvariants2022.split <- analysisData.GRSvariants2022 %>%
  mutate(`Variant (Sharp 2022)` = ifelse(grepl("DQ", `Variant (Sharp 2022)`)==TRUE, `Variant (Sharp 2022)`,
                                         paste(`Variant (Sharp 2022)`, " (", Score_Allele, ")", sep = ""))) %>%
  split(., .$`Variant (Sharp 2022)`)

analysisData.GRSvariants2019.split %>% tail(n=2)
analysisData.GRSvariants2022.split %>% tail(n=2)

analysisData.GRSvariants2019.split %>% names()
analysisData.GRSvariants2022.split %>% names()
```

#### Fit an additive genetic model for variant dosage with adjustment for the top 5 PCs for ancestry
```{r}
set.seed(1234)
fit.variants.GRS2019 <- map(analysisData.GRSvariants2019.split,
                            ~glm(Celiac ~ Dosage_for_GRS + PC1 + PC2 + PC3 + PC4 + PC5,
                                 family = binomial(link = "logit"),
                                 data = .))
set.seed(1234)
fit.variants.GRS2022 <- map(analysisData.GRSvariants2022.split,
                            ~glm(Celiac ~ Dosage_for_GRS + PC1 + PC2 + PC3 + PC4 + PC5,
                                 family = binomial(link = "logit"),
                                 data = .))
```

#### Create a results table for each version of the GRS
```{r}
results.variants.GRS2019 <- map2(.x = fit.variants.GRS2019,
                              .y = analysisData.GRSvariants2019.split,
                              .f = ~tidy(x = .x, conf.int=TRUE, data = .y)) %>%
  bind_rows(.id="Variant") %>%
  mutate(Model_Call= 'glm(Celiac ~ Dosage_for_GRS + PC1 + PC2 + PC3 + PC4 + PC5, family = binomial(link = "logit"), data = .)') %>%
  select(Model_Call, everything()) %>%
  filter(term == "Dosage_for_GRS") %>%
  #group_by(term) %>% # Group by term if you haven't already limited your results dataframe to the variable for which you want to calculate adjusted P-values
  mutate(Q = p.adjust(p.value, method = "BH")) %>%
  #ungroup() %>%
  mutate(GRS_version = analysisData.GRSvariants2022$GRS_version[1],
         GRS_reference = analysisData.GRSvariants2022$GRS_reference[1]) %>%
  select(GRS_version, GRS_reference, everything()) %>%
  mutate(Variant = ifelse(Variant == "X/X ()", "X/X", Variant)) %>%
  rename(logOR = estimate,
         P = p.value) %>%
  select(-c(std.error, statistic))

results.variants.GRS2022 <- map2(.x = fit.variants.GRS2022,
                              .y = analysisData.GRSvariants2022.split,
                              .f = ~tidy(x = .x, conf.int=TRUE, data = .y)) %>%
  bind_rows(.id="Variant") %>%
  mutate(Model_Call= 'glm(Celiac ~ Dosage_for_GRS + PC1 + PC2 + PC3 + PC4 + PC5, family = binomial(link = "logit"), data = .)') %>%
  select(Model_Call, everything()) %>%
  filter(term == "Dosage_for_GRS") %>%
  #group_by(term) %>% # Group by term first if you haven't already limited your results dataframe to the variable for which you want to calculate adjusted P-values
  mutate(Q = p.adjust(p.value, method = "BH")) %>%
  #ungroup() %>%
  mutate(GRS_version = analysisData.GRSvariants2022$GRS_version[1],
         GRS_reference = analysisData.GRSvariants2022$GRS_reference[1]) %>%
  select(GRS_version, GRS_reference, everything()) %>%
  mutate(Variant = ifelse(Variant == "X/X ()", "X/X", Variant)) %>%
  rename(logOR = estimate,
         P = p.value) %>%
  select(-c(std.error, statistic))
```

#### View and output the results table for each version of the GRS
```{r}
results.variants.GRS2019 %>% arrange(P)
results.variants.GRS2022 %>% arrange(P)

setwd(dir.Results)
fwrite(results.variants.GRS2019, "MEGA_041822_RESULTS_CDGRS2019_VariantDosage_vs_Celiac_v0.1_JRS.csv")
fwrite(results.variants.GRS2022, "MEGA_041822_RESULTS_CDGRS2022_VariantDosage_vs_Celiac_v0.1_JRS.csv")
```

##### Volcano plots

##### GRS 2019
```{r}
# results.variants.GRS2019

# Setting and modifying theme for plots
theme_set(theme_gray(base_size = 12, base_family = "Arial") +
            theme(panel.border = element_rect(colour="black", fill = "transparent"),
                  plot.title = element_text(face="bold", hjust = 0), # lineheight=.8, size=20,
                  axis.text = element_text(color="black", size = 14), 
                  axis.text.x = element_text(angle = 0, hjust = NULL),
                  strip.background = element_rect(colour="black", fill = "light grey", size = 1), # adjusts facet label borders (if any)
                  panel.background = element_blank(),
                  panel.grid = element_blank()
          ))
#

standard_colors <- c("#333333", "#009b4e") 

summary(results.variants.GRS2019$logOR)
#   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#-16.193   0.000   0.000  -0.270   0.000   1.471 
     
results.variants.GRS2019 %>%
  mutate(Q.Result_Category = ifelse(Q<0.10 & logOR>0, "Significantly up in DS with CD",
                                    ifelse(Q<0.10 & logOR<0, "Significantly down in DS with CD", "Not statistically significant")),
         P.Result_Category = ifelse(P<0.10 & logOR>0, "Significantly up in DS with CD",
                                    ifelse(P<0.10 & logOR<0, "Significantly down in DS with CD", "Not statistically significant"))) %>%
  arrange(desc(abs(logOR))) %>%
  mutate(rank_logOR = row_number()) %>%
  arrange(P) %>%
  mutate(rank_P = row_number()) %>%
  mutate(label = ifelse(rank_logOR<5 | rank_P<5, Variant, "")) %>%
  ggplot(aes(logOR, -log10(Q))) +
  geom_hline(yintercept = -log10(0.1), linetype = 2) +
  geom_vline(xintercept = 0, linetype = 2) +
  #geom_point(
  #  aes(color = "All")
  #  ) +
  geom_point(data = . %>% filter(Q.Result_Category=="Not statistically significant"),
             aes(), color = "dark gray") +
  geom_point(data = . %>% filter(Q.Result_Category=="Significantly up in DS with CD"),
             aes(), colour = "#153152") +
  geom_point(data = . %>% filter(Q.Result_Category=="Significantly down in DS with CD"),
             aes(), colour = "#006c9e") +
  xlim(-20, 20) +
  ylim(0,2) +
  # set colors
  xlab(bquote(''~log(OR)~'')) +
  ylab(bquote(''~-log[10](Q)~'')) +
  #ylab(bquote('-log10'~P[BH]~)) +
  scale_color_manual(values = c("Not statistically significant" = "grey",
                                "Significantly up in DS with CD" = "#153152",
                                "Significantly down in DS with CD" = "#006c9e"), name = NULL) +
  # Add gene labels
  geom_text_repel(aes(label = label)) + #, xlim = c(2,4)) +
  theme(aspect.ratio = 1.2,
        #legend.position = c(0.15, 0.9), # depends on data scales
        legend.position = "none" ) +
   #    legend.background = element_blank() ) +
  labs(title = "Variant association with Celiac in DS",
       caption = results.variants.GRS2019$Model_Call[1])

setwd(dir.GRSplots)
filename <- "MEGA_041822_VolcanoPlot_GRS2019_Celiac_vs_Dosage_adj5PCs_displayQvals_v0.1_JRS"
ggsave(filename = paste(filename, ".png", sep = ""),
       width = 5, height = 5, units = "in")
ggsave(filename = paste(filename, ".pdf", sep = ""),
       device = cairo_pdf, width = 5, height = 5, units = "in")

results.variants.GRS2019 %>%
  mutate(Q.Result_Category = ifelse(Q<0.10 & logOR>0, "Significantly up in DS with CD",
                                    ifelse(Q<0.10 & logOR<0, "Significantly down in DS with CD", "Not statistically significant")),
         P.Result_Category = ifelse(P<0.10 & logOR>0, "Significantly up in DS with CD",
                                    ifelse(P<0.10 & logOR<0, "Significantly down in DS with CD", "Not statistically significant"))) %>%
  arrange(desc(abs(logOR))) %>%
  mutate(rank_logOR = row_number()) %>%
  arrange(P) %>%
  mutate(rank_P = row_number()) %>%
  mutate(label = ifelse(rank_logOR<5 | rank_P<5, Variant, "")) %>%
  ggplot(aes(logOR, -log10(P))) +
  geom_hline(yintercept = -log10(0.1), linetype = 2) +
  geom_vline(xintercept = 0, linetype = 2) +
  #geom_point(
  #  aes(color = "All")
  #  ) +
  geom_point(data = . %>% filter(Q.Result_Category=="Not statistically significant"),
             aes(), color = "dark gray") +
  geom_point(data = . %>% filter(Q.Result_Category=="Significantly up in DS with CD"),
             aes(), colour = "#153152") +
  geom_point(data = . %>% filter(Q.Result_Category=="Significantly down in DS with CD"),
             aes(), colour = "#006c9e") +
  xlim(-20, 20) +
  ylim(0,2) +
  # set colors
  xlab(bquote(''~log(OR)~'')) +
  ylab(bquote(''~-log[10](P)~'')) +
  #ylab(bquote('-log10'~P[BH]~)) +
  scale_color_manual(values = c("Not statistically significant" = "grey",
                                "Significantly up in DS with CD" = "#153152",
                                "Significantly down in DS with CD" = "#006c9e"), name = NULL) +
  # Add gene labels
  geom_text_repel(aes(label = label)) + #, xlim = c(2,4)) +
  theme(aspect.ratio = 1.2,
        #legend.position = c(0.15, 0.9), # depends on data scales
        legend.position = "none" ) +
   #    legend.background = element_blank() ) +
  labs(title = "Variant association with Celiac in DS",
       caption = results.variants.GRS2019$Model_Call[1])

setwd(dir.GRSplots)
filename <- "MEGA_041822_VolcanoPlot_GRS2019_Celiac_vs_Dosage_adj5PCs_displayPvals_v0.1_JRS"
ggsave(filename = paste(filename, ".png", sep = ""),
       width = 5, height = 5, units = "in")
ggsave(filename = paste(filename, ".pdf", sep = ""),
       device = cairo_pdf, width = 5, height = 5, units = "in")
```

##### GRS 2022
```{r}

results.variants.GRS2022 %>%
  mutate(Q.Result_Category = ifelse(Q<0.10 & logOR>0, "Significantly up in DS with CD",
                                    ifelse(Q<0.10 & logOR<0, "Significantly down in DS with CD", "Not statistically significant")),
         P.Result_Category = ifelse(P<0.10 & logOR>0, "Significantly up in DS with CD",
                                    ifelse(P<0.10 & logOR<0, "Significantly down in DS with CD", "Not statistically significant"))) %>%
  arrange(desc(abs(logOR))) %>%
  mutate(rank_logOR = row_number()) %>%
  arrange(P) %>%
  mutate(rank_P = row_number()) %>%
  mutate(label = ifelse(rank_logOR<5 | rank_P<5, Variant, "")) %>%
  ggplot(aes(logOR, -log10(Q))) +
  geom_hline(yintercept = -log10(0.1), linetype = 2) +
  geom_vline(xintercept = 0, linetype = 2) +
  #geom_point(
  #  aes(color = "All")
  #  ) +
  geom_point(data = . %>% filter(Q.Result_Category=="Not statistically significant"),
             aes(), color = "dark gray") +
  geom_point(data = . %>% filter(Q.Result_Category=="Significantly up in DS with CD"),
             aes(), colour = "#153152") +
  geom_point(data = . %>% filter(Q.Result_Category=="Significantly down in DS with CD"),
             aes(), colour = "#006c9e") +
  xlim(-20, 20) +
  ylim(0,2) +
  # set colors
  xlab(bquote(''~log(OR)~'')) +
  ylab(bquote(''~-log[10](Q)~'')) +
  #ylab(bquote('-log10'~P[BH]~)) +
  scale_color_manual(values = c("Not statistically significant" = "grey",
                                "Significantly up in DS with CD" = "#153152",
                                "Significantly down in DS with CD" = "#006c9e"), name = NULL) +
  # Add gene labels
  geom_text_repel(aes(label = label)) + #, xlim = c(2,4)) +
  theme(aspect.ratio = 1.2,
        #legend.position = c(0.15, 0.9), # depends on data scales
        legend.position = "none" ) +
   #    legend.background = element_blank() ) +
  labs(title = "Variant association with Celiac in DS",
       caption = results.variants.GRS2022$Model_Call[1])

setwd(dir.GRSplots)
filename <- "MEGA_041822_VolcanoPlot_GRS2022_Celiac_vs_Dosage_adj5PCs_displayQvals_v0.1_JRS"
ggsave(filename = paste(filename, ".png", sep = ""),
       width = 5, height = 5, units = "in")
ggsave(filename = paste(filename, ".pdf", sep = ""),
       device = cairo_pdf, width = 5, height = 5, units = "in")

results.variants.GRS2022 %>%
  mutate(Q.Result_Category = ifelse(Q<0.10 & logOR>0, "Significantly up in DS with CD",
                                    ifelse(Q<0.10 & logOR<0, "Significantly down in DS with CD", "Not statistically significant")),
         P.Result_Category = ifelse(P<0.10 & logOR>0, "Significantly up in DS with CD",
                                    ifelse(P<0.10 & logOR<0, "Significantly down in DS with CD", "Not statistically significant"))) %>%
  arrange(desc(abs(logOR))) %>%
  mutate(rank_logOR = row_number()) %>%
  arrange(P) %>%
  mutate(rank_P = row_number()) %>%
  mutate(label = ifelse(rank_logOR<5 | rank_P<5, Variant, "")) %>%
  ggplot(aes(logOR, -log10(P))) +
  geom_hline(yintercept = -log10(0.1), linetype = 2) +
  geom_vline(xintercept = 0, linetype = 2) +
  #geom_point(
  #  aes(color = "All")
  #  ) +
  geom_point(data = . %>% filter(Q.Result_Category=="Not statistically significant"),
             aes(), color = "dark gray") +
  geom_point(data = . %>% filter(Q.Result_Category=="Significantly up in DS with CD"),
             aes(), colour = "#153152") +
  geom_point(data = . %>% filter(Q.Result_Category=="Significantly down in DS with CD"),
             aes(), colour = "#006c9e") +
  xlim(-20, 20) +
  ylim(0,2) +
  # set colors
  xlab(bquote(''~log(OR)~'')) +
  ylab(bquote(''~-log[10](P)~'')) +
  #ylab(bquote('-log10'~P[BH]~)) +
  scale_color_manual(values = c("Not statistically significant" = "grey",
                                "Significantly up in DS with CD" = "#153152",
                                "Significantly down in DS with CD" = "#006c9e"), name = NULL) +
  # Add gene labels
  geom_text_repel(aes(label = label)) + #, xlim = c(2,4)) +
  theme(aspect.ratio = 1.2,
        #legend.position = c(0.15, 0.9), # depends on data scales
        legend.position = "none" ) +
   #    legend.background = element_blank() ) +
  labs(title = "Variant association with Celiac in DS",
       caption = results.variants.GRS2022$Model_Call[1])

setwd(dir.GRSplots)
filename <- "MEGA_041822_VolcanoPlot_GRS2022_Celiac_vs_Dosage_adj5PCs_displayPvals_v0.1_JRS"
ggsave(filename = paste(filename, ".png", sep = ""),
       width = 5, height = 5, units = "in")
ggsave(filename = paste(filename, ".pdf", sep = ""),
       device = cairo_pdf, width = 5, height = 5, units = "in")

```




# ARCHIVE



##### Read in supplemental tables from Sharp et al., 2019
```{r}
setwd(dir)
Table_S2 <- read.xlsx("apt15826-sup-0001-supinfo.xlsx", sheet = "Table S2", startRow = 3) %>%
  separate(`Odds.Ratio.[95%.CI]`, into = c("OR", "CI"), sep = " ", extra = "merge", remove = TRUE) %>%
  rename(OR.GRS = OR,
         CI.GRS = CI,
         Weight.GRS = `Weight.(β)`)
Table_S2

setwd(dir)
Table_S3 <- read.xlsx("apt15826-sup-0001-supinfo.xlsx", sheet = "Table S3", startRow = 3) %>%
  rename(OR.GRS = OR,
         Weight.GRS = `Weight.(β)`)
Table_S3
```

#### Check that we have the expected number of Celiac cases in the analysis dataset
```{r}
analysisData %>%
  select(RecordID, Celiac) %>%
  unique() %>%
  group_by(Celiac) %>%
  summarise(N = n())

# Good.
```

#### Check if all of our participants have complete dosage for every HLA-DQ genotype and every non-HLA SNP
```{r}

```

#### Check for any samples with more than 1 LabID in the analysis data
```{r}

```

#### Prepare dataframe for analysis of GRS (excluding variant-specific data)
```{r}
analysisData.GRS <- analysisData %>%
  select(-c(Variant, Dosage, MEGA.VariantID,
            AT_GC_SNP, MEGA.CHR, MEGA.BP, Weight.GRS,)) %>%
  unique()

analysisData.GRS
```

#### Run logistic regression to test for association of GRS with Celiac status among T21s
```{r}
set.seed(1234)
fit.GRS <- glm(Celiac ~ GRS,
               family = binomial(link = "logit"),
               data = analysisData.GRS)

results.GRS <- fit.GRS %>% tidy(conf.int = TRUE) %>%
  select(-c(std.error, statistic)) %>%
  rename(Term = term,
         logOR = estimate,
         conf.low_logOR = conf.low,
         conf.high_logOR = conf.high,
         P = p.value) %>%
  mutate(OR = exp(logOR),
         conf.low_OR = exp(conf.low_logOR),
         conf.high_OR = exp(conf.high_logOR)) %>%
  select(-c(logOR, conf.low_logOR, conf.high_logOR)) %>%
  mutate(`95% CI` = paste("(", round(conf.low_OR, digits = 2), ", ", round(conf.high_OR, digits = 2), ")", sep = "")) %>%
  mutate(OR = round(OR, digits = 2),
         P = round(P, digits = 4)) %>%
  filter(Term == "GRS") %>%
  mutate(Term = gsub("GRS", "GRS*", Term)) %>%
  select(Term, OR, `95% CI`, P)

results.GRS
```

#### Run logistic regression to test for association of Celiac with the non-HLA-DQ component of the GRS
```{r}
set.seed(1234)
fit.GRS_2components <- glm(Celiac ~ GRS.HLADQ_component + GRS.nonHLADQ_component,
               family = binomial(link = "logit"),
               data = analysisData.GRS)

results.GRS_2components <- fit.GRS_2components %>% tidy(conf.int = TRUE) %>%
  select(-c(std.error, statistic)) %>%
  rename(Term = term,
         logOR = estimate,
         conf.low_logOR = conf.low,
         conf.high_logOR = conf.high,
         P = p.value) %>%
  mutate(OR = exp(logOR),
         conf.low_OR = exp(conf.low_logOR),
         conf.high_OR = exp(conf.high_logOR)) %>%
  select(-c(logOR, conf.low_logOR, conf.high_logOR)) %>%
  mutate(`95% CI` = paste("(", round(conf.low_OR, digits = 2), ", ", round(conf.high_OR, digits = 2), ")", sep = "")) %>%
  mutate(OR = round(OR, digits = 2),
         P = round(P, digits = 4)) %>%
  filter(Term == "GRS.HLADQ_component" | Term == "GRS.nonHLADQ_component") %>%
  mutate(Term = gsub("GRS.nonHLADQ_component", "GRS*, non-HLA-DQ component", Term),
         Term = gsub("GRS.HLADQ_component", "GRS*, HLA-DQ component", Term)) %>%
  select(Term, OR, `95% CI`, P)

results.GRS_2components
```

#### Create output object to write to multi-sheet XLSX workbook
```{r}
results.list <- list("GRS*" = results.GRS,
     "GRS*, split components" = results.GRS_2components)
```

#### Save results
```{R}
setwd(dir.Results)
write.xlsx(results.list, "Celiac_022322_RESULTS_Logistic_modifiedGRS_vs_CeliacStatus_v0.1_JRS.xlsx", overwrite=TRUE)

setwd(dir.Results)
fwrite(results.GRS, "Celiac_022322_RESULTS_Logistic_modifiedGRS_vs_CeliacStatus_v0.1_JRS.csv")
fwrite(results.GRS, "Celiac_022322_RESULTS_Logistic_modifiedGRS_vs_CeliacStatus_v0.1_JRS.tsv", sep = "\t")

setwd(dir.Results)
fwrite(results.GRS_2components, "Celiac_022322_RESULTS_Logistic_modifiedGRSsplit_vs_CeliacStatus_v0.1_JRS.csv")
fwrite(results.GRS_2components, "Celiac_022322_RESULTS_Logistic_modifiedGRSsplit_vs_CeliacStatus_v0.1_JRS.tsv", sep = "\t")
```


#### Plot ROC curves for GRS in DS
```{r}
# AUC from pROC::roc()
library(pROC)
resRoc <- roc(analysisData.GRS$Celiac ~ fit.GRS_2components$fitted)

# Create a basic roc object
rocobj <- roc(analysisData.GRS$Celiac ~ fit.GRS_2components$fitted, ci = TRUE, plot=FALSE)
rocobj$ci # 95% CI: 0.6146-0.8517 (DeLong)
ciobj <- ci.se(rocobj, specificities=seq(0, 1, l=25))
dat.ci <- data.frame(x = as.numeric(rownames(ciobj)),
                     lower = ciobj[, 1],
                     upper = ciobj[, 3])

theme_set(theme_gray(base_size = 12, base_family = "Arial") +
            theme(panel.border = element_rect(colour="black", fill = "transparent"),
                  plot.title = element_text(face="bold", hjust = 0, size = 11), # lineheight=.8, size=20,
                  axis.text = element_text(color="black", size = 11),
                  axis.text.x = element_text(angle = 0, hjust = NULL),
                  plot.caption = element_text(size = 8),
                  strip.background = element_rect(colour="black", fill = "light grey", size = 1), # adjusts facet label borders (if any)
                  panel.background = element_blank(),
                  panel.grid = element_blank()
          ))

g <- ggroc(rocobj,
           alpha = 0.9,
           ci = TRUE,
           colour = RedBlue[[1]],
           #colour = GrayBlue[[2]],
           #linetype = 2,
           size = 1,
           legacy.axes = TRUE
           ) +
  geom_segment(aes(x = 0, xend = 1, y = 0, yend = 1), color=GrayBlue[[2]]) + #, linetype="dashed")
  coord_equal() +
  labs(title = "Predictive accuracy of GRS* in DS",
       subtitle = paste("Area under the curve: ", round(resRoc$auc, digits = 4), sep = ""),
       caption = "Logistic: Celiac ~ GRS_HLADQ + GRS_nonHLADQ") +
  theme(plot.caption = element_text(hjust = 0)) +
  ylab("Sensitivity") +
  xlab("1 - Specificity")
g
filename <- "MEGA_022322_ROC_modifiedGRS_splitComponents_vs_CeliacStatus_v0.1_JRS"
setwd(dir.Figures)
ggsave(filename = paste(filename, ".png", sep = ""), width = 3.5, height = 3.5, units = "in")
setwd(dir.Figures)
ggsave(filename = paste(filename, ".pdf", sep = ""), device = cairo_pdf, width = 3.5, height = 3.5, units = "in")
```